def make_graph():
if PROBE_DIM == 0:
return complete_graph(N_SITES)
elif PROBE_DIM == 1:
x = [site.x for site in PROBE_SITES]
return path_graph(x)
elif PROBE_DIM == 2:
xy = [(site.x,site.y) for site in PROBE_SITES]
return triangle_graph(xy)
def make_graph():
if PROBE_DIM == 0:
return complete_graph(N_SITES)
elif PROBE_DIM == 1:
x = [site.x for site in PROBE_SITES]
return path_graph(x)
elif PROBE_DIM == 2:
xy = [(site.x, site.y) for site in PROBE_SITES]
return triangle_graph(xy)
def triangle_graph(Locs):
"""
Returns a graph giving the Delaunay triangulation of a set of two-dimensional points
Parameters
-------
Locs : ndarray, or anything that gets cast into ndarray upon np.array(Locs)
Locs.shape = (n,2), where n is the number of points
Returns
------
out : networkx Graph
"""
Locs = np.array(Locs)
if len(Locs)==1:
return complete_graph(1)
else:
Triangulation = sd.Triangulation(Locs[:,0],Locs[:,1])
return Triangulation.node_graph()
def triangle_graph(Locs):
"""
Returns a graph giving the Delaunay triangulation of a set of two-dimensional points
Parameters
-------
Locs : ndarray, or anything that gets cast into ndarray upon np.array(Locs)
Locs.shape = (n,2), where n is the number of points
Returns
------
out : networkx Graph
"""
Locs = np.array(Locs)
if len(Locs) == 1:
return complete_graph(1)
else:
Triangulation = sd.Triangulation(Locs[:, 0], Locs[:, 1])
return Triangulation.node_graph()
def path_graph(Locs):
"""
Returns a line graph for a set of one-dimensional locations.
Parameters
-------
Locs : ndarray, or anything that gets cast into ndarray upon np.array(Locs)
Returns
------
out : graph, i.e. dictionary key -> set(keys)
"""
Locs = np.array(Locs).flatten()
if len(Locs)==1:
return complete_graph(1)
else:
G = {}
SortInds = np.argsort(Locs)
for src,targ in zip(SortInds[1:],SortInds[:-1]):
add_edge(G,src,targ)
return G
def path_graph(Locs):
"""
Returns a line graph for a set of one-dimensional locations.
Parameters
-------
Locs : ndarray, or anything that gets cast into ndarray upon np.array(Locs)
Returns
------
out : graph, i.e. dictionary key -> set(keys)
"""
Locs = np.array(Locs).flatten()
if len(Locs) == 1:
return complete_graph(1)
else:
G = {}
SortInds = np.argsort(Locs)
for src, targ in zip(SortInds[1:], SortInds[:-1]):
add_edge(G, src, targ)
return G
